Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Abstract

Abstract Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Nodes of these networks are the target of epilepsy surgery. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography (iEEG) can quantify epileptogenicity and predict surgical outcome in children with drug-resistant epilepsy (DRE). We computed Amplitude Envelope Correlation (AEC) and Phase Locking Value (PLV) for different states (i.e., interictal with no spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and for different frequency bands. We then computed each node’s strength (i.e., AEC or PLV at iEEG electrodes). We observed differences in nodal strength among the different states following a hierarchical epileptogenic organization: lower FC in interictal and pre-ictal states followed by higher FC values in ictal and post-ictal states (p < 0.05). We also observed higher nodal strength within resection for patients with good outcome (n = 22, Engel I), but not for poor outcome (n = 9, Engel II-IV), for all states (except ictal) and all bands (p < 0.05). Resection of hubs with high nodal strength was predictive of outcome (75–92% positive and 47–63% negative predictive values). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in children with DRE.